### Curriculum vitae

A condensed curriculum vitae (as of Jan 24 2019) can be found here, or see my Google Scholar profile.

### Short bio

Marie E. Rognes is Chief Research Scientist at Simula Research Laboratory, Oslo, Norway. Her research focuses on numerical methods for partial differential equations, software for scientific computing, with applications in biomechanics and neuroscience. She is an ERC Starting Grantee (2017-2022), winner of the 2015 Wilkinson Prize for Numerical Software, winner of the 2018 Royal Norwegian Society of Sciences and Letters Prize for Young Researchers within the Natural Sciences, core member of the FEniCS Project and Dolfin-adjoint teams, and Founding Member of the Young Academy of Norway (2015-2019).

Rognes received her Master (2005) and Ph.D. (2009) degrees in applied mathematics at the Centre for Mathematics for Applications, Faculty of Mathematics and Natural Science, University of Oslo. At Simula, she has led the Biomedical Computing Department (2012-2016), the Robust Solvers project at the CoE Center for Biomedical Computing (2012-2017), the AUQ-PDE project (2015-2017), the FRIPRO Young Research Talent project Waterscape (2016-2020), and the ERC Starting Grant project Waterscales (2016-2022). In 2015-2016, Rognes was an Adjunct Associate Professor in Solid Mechanics at the Department of Mathematics at the University of Oslo. In May 2017, Rognes gave a talk at TEDxOslo titled “Mathematics that cures us” on the topic of mathematical modelling in medicine.

### Publications

For a relatively recent and complete list of articles, book chapters, and scientific presentations, see my list of publications (2019-08-23).

#### Articles in international journals

[30] V. Vinje, A. Eklund, K.-A. Mardal, M. E. Rognes and K.-H. Støverud. Intracranial pressure elevation alters CSF clearance pathways. Submitted for publication, 2019.

[29] M. Croci, V. Vinje and M. E. Rognes. Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Accepted by Fluids and Barriers of the Central Nervous System, 2019.

[28] X. Lai et al, Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data, Cancer Research, 2019.

[27] V. Vinje, G. Ringstad, E. K. Lindstrom, L. M. Valnes, M. E. Rognes, P. K. Eide and K.-A. Mardal. Respiratory influence on cerebrospinal fluid flow – a computational study based on long-term intracranial pressure measurements. Nature Scientific Reports, 2019.

[26] P. E. Farrell, J. E. Hake, S. W. Funke and M. E. Rognes. Automated adjoints of coupled PDE-ODE systems. SIAM Journal on Scientific Computing, 2019.

[25] J. J. Lee, E. Piersanti, K.-A. Mardal and M. E. Rognes. A mixed finite element method for nearly incompressible multiple-network poroelasticity. SIAM Journal on Scientific Computing, 2019.

[24] R. Rodriguez-Cantano, J. Sundnes and M. E. Rognes. Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response, International Journal for Numerical Methods in Biomedical Engineering, 2019.

[23] V. Vinje, J. Brucker, M. E. Rognes, K. A. Mardal, V. Haughton. Fluid dynamics in syringomyelia cavities: Effects of heart rate, CSF velocity, CSF velocity waveform and craniovertebral decompression, The Neuroradiology Journal, 2018.

[22] M. Croci, M. B. Giles, M. E. Rognes and P. E. Farrell. Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes. SIAM/ASA Journal Uncertainty Quantification, 2018.

[21] G. Balaban, H. Finsberg, S. Funke, T. F. Håland, E. Hopp, J. Sundnes, S. Wall and M. E. Rognes. In vivo estimation of elastic heterogeneity in an infarcted human heart, Biomechanics and Modeling in Mechanobiology, 2018.

[20] A. Tveito, K. H. Jæger, M. Kuchta, K.-A. Mardal and M. E. Rognes. A cell-based framework for numerical modelling of electrical conduction in cardiac tissue. Frontiers in Physics, Computational Physics, 2017.

[19] G. Pizzichelli, B. Kehlet, Ø. Evju, B. Martin, M. E. Rognes, K.-A. Mardal and E. Sinibaldi. Numerical study of intrathecal drug delivery to a permeable spinal cord: effect of catheter position and angle. Computer Methods in Biomechanics and Biomedical Engineering, 2017

[18] S. Kallhovd, M. M. Maleckar and M. E. Rognes. Inverse estimation of cardiac activation times via gradient-based optimisation. International Journal for Numerical Methods in Biomedical Engineering, 2017.

[17] M. E. Rognes, P. E. Farrell, S. W. Funke, J. E. Hake and M. M. C. Maleckar. cbcbeat: an adjoint-enabled framework for computational cardiac electrophysiology. Journal for Open Source Software, 2017.

[16] G. Balaban, H. Finsberg, H. H. Odland, M. E. Rognes, S. Ross, J. Sundnes and S. Wall. High resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. International Journal for Numerical Methods in Biomedical Engineering, 2017.

[15] G. Balaban, M. S. Alnæs, J. Sundnes and M. E. Rognes. Adjoint multi-start based estimation of cardiac hyperelastic material parameters using shear data. Biomechanics and Modeling in Mechanobiology, vol. 15(6), pp. 1509-1521, 2016.

[14] M. Alnæs, J. Blechta, J. Hake, A. Johansson, B. Kehlet, A. Logg, C. Richardson, J. Ring, M. E. Rognes and G. N. Wells. The FEniCS Project Version 1.5. Archive of Numerical Software, vol. 3(100), 2015.

[13] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A Nitsche-based cut finite element method for a fluid-structure interaction problem. Communications in Applied Mathematics and Computational Science, vol. 10(2), pp. 97-120, 2015.

[12] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A stabilized Nitsche overlapping mesh method for the Stokes problem. Numerische Mathematik, vol. 128(1), pp. 73–101, 2014.

[11] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A stabilized Nitsche fictitious domain method for the Stokes problem. Journal of Scientific Computing, vol. 61(3), pp. 604–628, 2014.

[10] M. S. Alnæs, A. Logg, K. B. Ølgaard, M. E. Rognes and G. N. Wells. Unified Form Language: A domain-specific language for weak formulations of partial differential equations. ACM Transactions on Mathematical Software, vol. 40(2), 2014.

[9] M. E. Rognes, D. A. Ham, C. J. Cotter and A. T. T. McRae. Automating the solution of PDEs on the sphere and other manifolds in FEniCS 1.2. Geoscientific Model Development, vol. 6, pp. 2099–2119, 2013.

[8] P. E. Farrell, D. A. Ham, S. W. Funke and M. E. Rognes. Automated derivation of the adjoint of high-level transient finite element programs. SIAM Journal on Scientific Computing, vol. 35(4), pp. 369–393, 2013.

[7] M. E. Rognes and A. Logg. Automated goal-oriented error control I: stationary variational problems. SIAM Journal on Scientific Computing, vol. 35(3), pp. 173–193, 2013.

[6] L. Vynnytska, M. E. Rognes and S. R. Clark. Benchmarking FEniCS for mantle convection simulations. Computers & Geosciences, vol. 50, pp. 95-105, 2013.

[5] A. Tveito, G. T. Lines, M. E. Rognes and M. M. Maleckar. An analysis of the shock strength needed to achieve defibrillation in a simplified mathematical model of cardiac tissue. International Journal of Numerical Analysis and Modeling, vol. 9(3), pp. 644–657, 2012.

[4] M. E. Rognes and R. Winther. Mixed finite element methods for linear viscoelasticity with weak symmetry. Mathematical Models and Methods in Applied Science, vol. 20(6), pp. 955–985, 2010.

[3] M. E. Rognes, M. C. Calderer and C. A. Micek. Modelling of and mixed finite element methods for gels in biomedical applications. SIAM Journal of Applied Mathematics, vol. 70(4), pp. 1305–1329, 2009.

[2] M. E. Rognes, R. C. Kirby and A. Logg. Efficient assembly of H(div) and H(curl) conforming finite elements. SIAM Journal on Scientific Computing, vol. 36(6), pp. 4130–4151, 2009.

[1] D. N. Arnold and M. E. Rognes. Stability of Lagrange elements for the mixed Laplacian. Calcolo, vol. 46(4), pp. 245–260, 2009.

#### Chapters in books

[A5] G. Halnes, K. H. Pettersen, L. Øyehaug, M. E. Rognes and G. T. Einevoll. Astrocytic ion dynamics: implications for potassium buffering and liquid flow. Computational Glioscience, Springer Series in Computational Neuroscience, edited by M. D. Pitta and H. Berry, Springer, 2019.

[A4] M. E. Rognes. Automated Testing of Saddle Point Stability Conditions. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A3] A. Logg, K. B. Ølgaard, M. E. Rognes and G. N. Wells. FFC: the FEniCS Form Compiler. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A2] R. C. Kirby, A. Logg, M. E. Rognes and A. R. Terrel. Common and Unusual Finite Elements. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A1] L. Vynnytska, S. R. Clark and M. E. Rognes. Dynamic Simulations of Convection in the Earth’s Mantle. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.